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Article

Retrieval of High-Resolution Soil Moisture through Combination of Sentinel-1 and Sentinel-2 Data

1
Hydrology, Agriculture and Land Observation Group (HALO), Water Desalination and Reuse Center, Division of Biological and Environmental Sciences and Engineering, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
2
Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(14), 2303; https://doi.org/10.3390/rs12142303
Received: 30 April 2020 / Revised: 13 July 2020 / Accepted: 14 July 2020 / Published: 17 July 2020
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
Estimating soil moisture based on synthetic aperture radar (SAR) data remains challenging due to the influences of vegetation and surface roughness. Here we present an algorithm that simultaneously retrieves soil moisture, surface roughness and vegetation water content by jointly using high-resolution Sentinel-1 SAR and Sentinel-2 multispectral imagery, with an application directed towards the provision of information at the precision agricultural scale. Sentinel-2-derived vegetation water indices are investigated and used to quantify the backscatter resulting from the vegetation canopy. The proposed algorithm then inverts the water cloud model to simultaneously estimate soil moisture and surface roughness by minimizing a cost function constructed by model simulations and SAR observations. To examine the performance of VV- and VH-polarized backscatters on soil moisture retrievals, three retrieval schemes are explored: a single channel algorithm using VV (SCA-VV) and VH (SCA-VH) polarizations and a dual channel algorithm using both VV and VH polarizations (DCA-VVVH). An evaluation of the approach using a combination of a cosmic-ray soil moisture observing system (COSMOS) and Soil Climate Analysis Network measurements over Nebraska shows that the SCA-VV scheme yields good agreement at both the COSMOS footprint and single-site scales. The features of the algorithms that have the most impact on the retrieval accuracy include the vegetation water content estimation scheme, parameters of the water cloud model and the specification of initial ranges of soil moisture and roughness, all of which are comprehensively analyzed and discussed. Through careful consideration and selection of these factors, we demonstrate that the proposed SCA-VV approach can provide reasonable soil moisture retrievals, with RMSE ranging from 0.039 to 0.078 m3/m3 and R2 ranging from 0.472 to 0.665, highlighting the utility of SAR for application at the precision agricultural scale. View Full-Text
Keywords: synthetic aperture radar; precision agriculture; microwave remote sensing; soil moisture synthetic aperture radar; precision agriculture; microwave remote sensing; soil moisture
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MDPI and ACS Style

Ma, C.; Li, X.; McCabe, M.F. Retrieval of High-Resolution Soil Moisture through Combination of Sentinel-1 and Sentinel-2 Data. Remote Sens. 2020, 12, 2303. https://doi.org/10.3390/rs12142303

AMA Style

Ma C, Li X, McCabe MF. Retrieval of High-Resolution Soil Moisture through Combination of Sentinel-1 and Sentinel-2 Data. Remote Sensing. 2020; 12(14):2303. https://doi.org/10.3390/rs12142303

Chicago/Turabian Style

Ma, Chunfeng, Xin Li, and Matthew F. McCabe 2020. "Retrieval of High-Resolution Soil Moisture through Combination of Sentinel-1 and Sentinel-2 Data" Remote Sensing 12, no. 14: 2303. https://doi.org/10.3390/rs12142303

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